

'''simple docstring'''
import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import YolosForObjectDetection, YolosModel
     from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor

class 						__magic_name__   :







 def __init__(       self  , snake_case_  , snake_case_=13  , snake_case_=[30, 30]  , snake_case_=2  , snake_case_=3  , snake_case_=True  , snake_case_=True  , snake_case_=32  , snake_case_=5  , snake_case_=4  , snake_case_=37  , snake_case_="gelu"  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=10  , snake_case_=0.02  , snake_case_=3  , snake_case_=None  , snake_case_=8  , snake_case_=10  , ):
   lowercase									=parent
   lowercase									=batch_size
   lowercase									=image_size
   lowercase									=patch_size
   lowercase									=num_channels
   lowercase									=is_training
   lowercase									=use_labels
   lowercase									=hidden_size
   lowercase									=num_hidden_layers
   lowercase									=num_attention_heads
   lowercase									=intermediate_size
   lowercase									=hidden_act
   lowercase									=hidden_dropout_prob
   lowercase									=attention_probs_dropout_prob
   lowercase									=type_sequence_label_size
   lowercase									=initializer_range
   lowercase									=num_labels
   lowercase									=scope
   lowercase									=n_targets
   lowercase									=num_detection_tokens
   # we set the expected sequence length (which is used in several tests)
   # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
   lowercase									=(image_size[1] // patch_size) * (image_size[0] // patch_size)
   lowercase									=num_patches + 1 + self.num_detection_tokens







 def 						_A(       self   ):
   lowercase									=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]   )

   lowercase									=None
   if self.use_labels:
     # labels is a list of Dict (each Dict being the labels for a given example in the batch)
     lowercase									=[]
     for i in range(self.batch_size   ):
       lowercase									={}
       lowercase									=torch.randint(
           high=self.num_labels  , size=(self.n_targets,)  , device=snake_case_   )
       lowercase									=torch.rand(self.n_targets  , 4  , device=snake_case_   )
       labels.append(snake_case_   )

   lowercase									=self.get_config()

   return config, pixel_values, labels







 def 						_A(       self   ):
   return YolosConfig(
       image_size=self.image_size  , patch_size=self.patch_size  , num_channels=self.num_channels  , hidden_size=self.hidden_size  , num_hidden_layers=self.num_hidden_layers  , num_attention_heads=self.num_attention_heads  , intermediate_size=self.intermediate_size  , hidden_act=self.hidden_act  , hidden_dropout_prob=self.hidden_dropout_prob  , attention_probs_dropout_prob=self.attention_probs_dropout_prob  , is_decoder=snake_case_  , initializer_range=self.initializer_range  , num_detection_tokens=self.num_detection_tokens  , num_labels=self.num_labels  , )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase									=YolosModel(config=snake_case_   )
   model.to(snake_case_   )
   model.eval()
   lowercase									=model(snake_case_   )
   self.parent.assertEqual(
       result.last_hidden_state.shape  , (self.batch_size, self.expected_seq_len, self.hidden_size)   )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase									=YolosForObjectDetection(snake_case_   )
   model.to(snake_case_   )
   model.eval()

   lowercase									=model(pixel_values=snake_case_   )
   lowercase									=model(snake_case_   )

   self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_detection_tokens, self.num_labels + 1)   )
   self.parent.assertEqual(result.pred_boxes.shape  , (self.batch_size, self.num_detection_tokens, 4)   )

   lowercase									=model(pixel_values=snake_case_  , labels=snake_case_   )

   self.parent.assertEqual(result.loss.shape  , ()   )
   self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_detection_tokens, self.num_labels + 1)   )
   self.parent.assertEqual(result.pred_boxes.shape  , (self.batch_size, self.num_detection_tokens, 4)   )







 def 						_A(       self   ):
   lowercase									=self.prepare_config_and_inputs()
   lowercase							, lowercase							, lowercase									=config_and_inputs
   lowercase									={'''pixel_values''': pixel_values}
   return config, inputs_dict

@require_torch
class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				unittest.TestCase ):
 UpperCamelCase__     =       (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 UpperCamelCase__     =       (
     {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
 )

 UpperCamelCase__     =       False
 UpperCamelCase__     =       False
 UpperCamelCase__     =       False
 UpperCamelCase__     =       False







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_=False   ):
   lowercase									=super()._prepare_for_class(snake_case_  , snake_case_  , return_labels=snake_case_   )

   if return_labels:
     if model_class.__name__ == "YolosForObjectDetection":
       lowercase									=[]
       for i in range(self.model_tester.batch_size   ):
         lowercase									={}
         lowercase									=torch.ones(
             size=(self.model_tester.n_targets,)  , device=snake_case_  , dtype=torch.long   )
         lowercase									=torch.ones(
             self.model_tester.n_targets  , 4  , device=snake_case_  , dtype=torch.float   )
         labels.append(snake_case_   )
       lowercase									=labels

   return inputs_dict







 def 						_A(       self   ):
   lowercase									=YolosModelTester(self   )
   lowercase									=ConfigTester(self  , config_class=snake_case_  , has_text_modality=snake_case_  , hidden_size=37   )







 def 						_A(       self   ):
   self.config_tester.run_common_tests()







 def 						_A(       self   ):
   # YOLOS does not use inputs_embeds
   pass







 def 						_A(       self   ):
   lowercase							, lowercase									=self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowercase									=model_class(snake_case_   )
     self.assertIsInstance(model.get_input_embeddings()  , (nn.Module)   )
     lowercase									=model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(snake_case_  , nn.Linear   )   )







 def 						_A(       self   ):
   lowercase							, lowercase									=self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowercase									=model_class(snake_case_   )
     lowercase									=inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowercase									=[*signature.parameters.keys()]

     lowercase									=['''pixel_values''']
     self.assertListEqual(arg_names[:1]  , snake_case_   )







 def 						_A(       self   ):
   lowercase									=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case_   )







 def 						_A(       self   ):
   lowercase							, lowercase									=self.model_tester.prepare_config_and_inputs_for_common()
   lowercase									=True

   # in YOLOS, the seq_len is different
   lowercase									=self.model_tester.expected_seq_len
   for model_class in self.all_model_classes:
     lowercase									=True
     lowercase									=False
     lowercase									=True
     lowercase									=model_class(snake_case_   )
     model.to(snake_case_   )
     model.eval()
     with torch.no_grad():
       lowercase									=model(**self._prepare_for_class(snake_case_  , snake_case_   )   )
     lowercase									=outputs.attentions
     self.assertEqual(len(snake_case_   )  , self.model_tester.num_hidden_layers   )

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     lowercase									=True
     lowercase									=model_class(snake_case_   )
     model.to(snake_case_   )
     model.eval()
     with torch.no_grad():
       lowercase									=model(**self._prepare_for_class(snake_case_  , snake_case_   )   )
     lowercase									=outputs.attentions
     self.assertEqual(len(snake_case_   )  , self.model_tester.num_hidden_layers   )

     self.assertListEqual(
         list(attentions[0].shape[-3:]   )  , [self.model_tester.num_attention_heads, seq_len, seq_len]  , )
     lowercase									=len(snake_case_   )

     # Check attention is always last and order is fine
     lowercase									=True
     lowercase									=True
     lowercase									=model_class(snake_case_   )
     model.to(snake_case_   )
     model.eval()
     with torch.no_grad():
       lowercase									=model(**self._prepare_for_class(snake_case_  , snake_case_   )   )

     lowercase									=1
     self.assertEqual(out_len + added_hidden_states  , len(snake_case_   )   )

     lowercase									=outputs.attentions

     self.assertEqual(len(snake_case_   )  , self.model_tester.num_hidden_layers   )
     self.assertListEqual(
         list(self_attentions[0].shape[-3:]   )  , [self.model_tester.num_attention_heads, seq_len, seq_len]  , )







 def 						_A(       self   ):
   def check_hidden_states_output(snake_case_  , snake_case_  , snake_case_   ):
     lowercase									=model_class(snake_case_   )
     model.to(snake_case_   )
     model.eval()

     with torch.no_grad():
       lowercase									=model(**self._prepare_for_class(snake_case_  , snake_case_   )   )

     lowercase									=outputs.hidden_states

     lowercase									=getattr(
         self.model_tester  , '''expected_num_hidden_layers'''  , self.model_tester.num_hidden_layers + 1   )
     self.assertEqual(len(snake_case_   )  , snake_case_   )

     # YOLOS has a different seq_length
     lowercase									=self.model_tester.expected_seq_len

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]   )  , [seq_length, self.model_tester.hidden_size]  , )

   lowercase							, lowercase									=self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowercase									=True
     check_hidden_states_output(snake_case_  , snake_case_  , snake_case_   )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowercase									=True

     check_hidden_states_output(snake_case_  , snake_case_  , snake_case_   )







 def 						_A(       self   ):
   lowercase									=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_object_detection(*snake_case_   )







 @slow
 def 						_A(       self   ):
   for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase									=YolosModel.from_pretrained(snake_case_   )
     self.assertIsNotNone(snake_case_   )




def 	UpperCamelCase   (       )		->	Tuple:




  '''simple docstring'''



  lowercase									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image




@require_torch
@require_vision
class 						__magic_name__   (							unittest.TestCase ):







 @cached_property
 def 						_A(       self   ):
   return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''   ) if is_vision_available() else None







 @slow
 def 						_A(       self   ):
   lowercase									=YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''   ).to(snake_case_   )

   lowercase									=self.default_image_processor
   lowercase									=prepare_img()
   lowercase									=image_processor(images=snake_case_  , return_tensors='''pt'''   ).to(snake_case_   )

   # forward pass
   with torch.no_grad():
     lowercase									=model(inputs.pixel_values   )

   # verify outputs
   lowercase									=torch.Size((1, 1_00, 92)   )
   self.assertEqual(outputs.logits.shape  , snake_case_   )

   lowercase									=torch.tensor(
       [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]  , device=snake_case_  , )
   lowercase									=torch.tensor(
       [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]  , device=snake_case_   )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  , snake_case_  , atol=1E-4   )   )
   self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]  , snake_case_  , atol=1E-4   )   )

   # verify postprocessing
   lowercase									=image_processor.post_process_object_detection(
       snake_case_  , threshold=0.3  , target_sizes=[image.size[::-1]]   )[0]
   lowercase									=torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]   ).to(snake_case_   )
   lowercase									=[75, 75, 17, 63, 17]
   lowercase									=torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]   ).to(snake_case_   )

   self.assertEqual(len(results['''scores''']   )  , 5   )
   self.assertTrue(torch.allclose(results['''scores''']  , snake_case_  , atol=1E-4   )   )
   self.assertSequenceEqual(results['''labels'''].tolist()  , snake_case_   )
   self.assertTrue(torch.allclose(results['''boxes'''][0, :]  , snake_case_   )   )

import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCamelCase__  =					{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False
  elif args.student_type == "gpt2":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[str]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Training" )
  parser.add_argument("--force"    ,     action="store_true"    ,     help="Overwrite dump_path if it already exists." )

  parser.add_argument(
      "--dump_path"    ,     type=a__    ,     required=a__    ,     help="The output directory (log, checkpoints, parameters, etc.)" )
  parser.add_argument(
      "--data_file"    ,     type=a__    ,     required=a__    ,     help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."    ,     )

  parser.add_argument(
      "--student_type"    ,     type=a__    ,     choices=["distilbert", "roberta", "gpt2"]    ,     required=a__    ,     help="The student type (DistilBERT, RoBERTa)."    ,     )
  parser.add_argument("--student_config"    ,     type=a__    ,     required=a__    ,     help="Path to the student configuration." )
  parser.add_argument(
      "--student_pretrained_weights"    ,     default=a__    ,     type=a__    ,     help="Load student initialization checkpoint." )

  parser.add_argument(
      "--teacher_type"    ,     choices=["bert", "roberta", "gpt2"]    ,     required=a__    ,     help="Teacher type (BERT, RoBERTa)." )
  parser.add_argument("--teacher_name"    ,     type=a__    ,     required=a__    ,     help="The teacher model." )

  parser.add_argument("--temperature"    ,     default=2.0    ,     type=a__    ,     help="Temperature for the softmax temperature." )
  parser.add_argument(
      "--alpha_ce"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the distillation loss. Must be >=0." )
  parser.add_argument(
      "--alpha_mlm"    ,     default=0.0    ,     type=a__    ,     help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."    ,     )
  parser.add_argument("--alpha_clm"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the CLM loss. Must be >=0." )
  parser.add_argument("--alpha_mse"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the MSE loss. Must be >=0." )
  parser.add_argument(
      "--alpha_cos"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the cosine embedding loss. Must be >=0." )

  parser.add_argument(
      "--mlm"    ,     action="store_true"    ,     help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
  parser.add_argument(
      "--mlm_mask_prop"    ,     default=0.15    ,     type=a__    ,     help="Proportion of tokens for which we need to make a prediction."    ,     )
  parser.add_argument("--word_mask"    ,     default=0.8    ,     type=a__    ,     help="Proportion of tokens to mask out." )
  parser.add_argument("--word_keep"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to keep." )
  parser.add_argument("--word_rand"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to randomly replace." )
  parser.add_argument(
      "--mlm_smoothing"    ,     default=0.7    ,     type=a__    ,     help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."    ,     )
  parser.add_argument("--token_counts"    ,     type=a__    ,     help="The token counts in the data_file for MLM." )

  parser.add_argument(
      "--restrict_ce_to_mask"    ,     action="store_true"    ,     help="If true, compute the distillation loss only the [MLM] prediction distribution."    ,     )
  parser.add_argument(
      "--freeze_pos_embs"    ,     action="store_true"    ,     help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."    ,     )
  parser.add_argument(
      "--freeze_token_type_embds"    ,     action="store_true"    ,     help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."    ,     )

  parser.add_argument("--n_epoch"    ,     type=a__    ,     default=3    ,     help="Number of pass on the whole dataset." )
  parser.add_argument("--batch_size"    ,     type=a__    ,     default=5    ,     help="Batch size (for each process)." )
  parser.add_argument(
      "--group_by_size"    ,     action="store_false"    ,     help="If true, group sequences that have similar length into the same batch. Default is true."    ,     )

  parser.add_argument(
      "--gradient_accumulation_steps"    ,     type=a__    ,     default=50    ,     help="Gradient accumulation for larger training batches."    ,     )
  parser.add_argument("--warmup_prop"    ,     default=0.05    ,     type=a__    ,     help="Linear warmup proportion." )
  parser.add_argument("--weight_decay"    ,     default=0.0    ,     type=a__    ,     help="Weight decay if we apply some." )
  parser.add_argument("--learning_rate"    ,     default=5e-4    ,     type=a__    ,     help="The initial learning rate for Adam." )
  parser.add_argument("--adam_epsilon"    ,     default=1e-6    ,     type=a__    ,     help="Epsilon for Adam optimizer." )
  parser.add_argument("--max_grad_norm"    ,     default=5.0    ,     type=a__    ,     help="Max gradient norm." )
  parser.add_argument("--initializer_range"    ,     default=0.02    ,     type=a__    ,     help="Random initialization range." )

  parser.add_argument(
      "--fp16"    ,     action="store_true"    ,     help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"    ,     )
  parser.add_argument(
      "--fp16_opt_level"    ,     type=a__    ,     default="O1"    ,     help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )    ,     )
  parser.add_argument("--n_gpu"    ,     type=a__    ,     default=1    ,     help="Number of GPUs in the node." )
  parser.add_argument("--local_rank"    ,     type=a__    ,     default=-1    ,     help="Distributed training - Local rank" )
  parser.add_argument("--seed"    ,     type=a__    ,     default=56    ,     help="Random seed" )

  parser.add_argument("--log_interval"    ,     type=a__    ,     default=500    ,     help="Tensorboard logging interval." )
  parser.add_argument("--checkpoint_interval"    ,     type=a__    ,     default=4_000    ,     help="Checkpoint interval." )
  _UpperCamelCase           =    parser.parse_args()
  sanity_checks(a__ )

  # ARGS #
  init_gpu_params(a__ )
  set_seed(a__ )
  if args.is_master:
    if os.path.exists(args.dump_path ):
      if not args.force:
        raise ValueError(
            f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
            " itUse `--force` if you want to overwrite it" )
      else:
        shutil.rmtree(args.dump_path )

    if not os.path.exists(args.dump_path ):
      os.makedirs(args.dump_path )
    logger.info(f'Experiment will be dumped and logged in {args.dump_path}' )

    # SAVE PARAMS #
    logger.info(f'Param: {args}' )
    with open(os.path.join(args.dump_path    ,     "parameters.json" )    ,     "w" ) as f:
      json.dump(vars(a__ )    ,     a__    ,     indent=4 )
    git_log(args.dump_path )

  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.student_type]
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  _UpperCamelCase           =    teacher_tokenizer_class.from_pretrained(args.teacher_name )
  _UpperCamelCase           =    {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    _UpperCamelCase           =    tokenizer.all_special_tokens.index(a__ )
    _UpperCamelCase           =    tokenizer.all_special_ids[idx]
  logger.info(f'Special tokens {special_tok_ids}' )
  _UpperCamelCase           =    special_tok_ids
  _UpperCamelCase           =    tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f'Loading data from {args.data_file}' )
  with open(args.data_file    ,     "rb" ) as fp:
    _UpperCamelCase           =    pickle.load(a__ )

  if args.mlm:
    logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)' )
    with open(args.token_counts    ,     "rb" ) as fp:
      _UpperCamelCase           =    pickle.load(a__ )

    _UpperCamelCase           =    np.maximum(a__    ,     1 ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      _UpperCamelCase           =    0.0  # do not predict special tokens
    _UpperCamelCase           =    torch.from_numpy(a__ )
  else:
    _UpperCamelCase           =    None

  _UpperCamelCase           =    LmSeqsDataset(params=a__    ,     data=a__ )
  logger.info("Data loader created." )

  # STUDENT #
  logger.info(f'Loading student config from {args.student_config}' )
  _UpperCamelCase           =    student_config_class.from_pretrained(args.student_config )
  _UpperCamelCase           =    True

  if args.student_pretrained_weights is not None:
    logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
    _UpperCamelCase           =    student_model_class.from_pretrained(args.student_pretrained_weights    ,     config=a__ )
  else:
    _UpperCamelCase           =    student_model_class(a__ )

  if args.n_gpu > 0:
    student.to(f'cuda:{args.local_rank}' )
  logger.info("Student loaded." )

  # TEACHER #
  _UpperCamelCase           =    teacher_model_class.from_pretrained(args.teacher_name    ,     output_hidden_states=a__ )
  if args.n_gpu > 0:
    teacher.to(f'cuda:{args.local_rank}' )
  logger.info(f'Teacher loaded from {args.teacher_name}.' )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(a__    ,     a__ )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(a__    ,     a__ )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0 ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  _UpperCamelCase           =    Distiller(
      params=a__    ,     dataset=a__    ,     token_probs=a__    ,     student=a__    ,     teacher=a__ )
  distiller.train()
  logger.info("Let's go get some drinks." )


if __name__ == "__main__":
   main()




